CUSTOMER & MARKETING INTELLIGENCE SERVICESCustomer Targeting Strategy Development

Case Studies

Customer Intelligence

Identifying Most Valuable Customer

Case StudyClient: A US based high end luxury retailer in the space of apparels and accessories

Business Context & Client Problem:The retailer has several high end luxury retail stores in key cities across the US and sell luxury apparels and related accessories. They want to identify the most valuable customers within their existing customers based on relationship and purchase patterns for proactive relationship management purpose. They also wanted to assess the profile of such best customers and catch them young in their lifecycle.

Impact on Business:The study not only identified the sweet spot of the business , but also created detailed understanding of best customer profile and characteristics and helped the retailer to create effective CRM program

Solution: Customer segmentation based on relationship quotient and identification of demographic and behavioural sweet spot for most valuable customers

Relationship Segmentation Segment Drill down Identify Look Alikes

• Analysing various transaction patterns, cycles, mix etc.

• Design segmentation scheme based on Recency, Frequency and Monetary factors

• Identify the Most Valuable Customer segment covering 80% of the business value with 20% customers

Understanding behavioral and demographic characteristics of the best customers against rest

Create scoring model to identify customers who look like the most valuable customers, however not yet reaching the status

Customer Intelligence

Targeted Cross Sell

Client: A leading global technology company

Business Context & Client Problem:A global technology giant wanted to cross-sell profitable docking stations to some of their existing SMB (Small & Medium Business) customers. Due to budget constraints, the company wanted to be focused on reaching out to the right set of accounts based on their propensity of buying a docking station in the near future. This could be derived through the relationship quotient, product purchase sequence as well as the estimated need of the company.

Impact on Business:A targeted cross-sell campaign based on relationship, product purchased so far and industry dynamics helped the sales people to not only focus on high RoI accounts, but also helped them customise their communication

Solution: A scoring model to prioritise the set of accounts based on R-F-M segmentation as well as natural product association between docking station and other products

Relationship Segmentation Product Association Analysis Scoring Model

• Analysing requency, frequency and monetary aspects of relationship with the accounts

• Design segmentation scheme based on RFM characteristics

• Identify sweet-spot for best customers

Identifying association between various products based on how often they are bought by same customer

Weighted Model incorporating relationship quotient, installed base association and industry dynamics

Case Study

Customer IntelligencePrediction of New Product Sales Trajectory Leveraging Social Media Buzz &

Sentiments

Client: Global marketing organisation of a leading manufacturer of personal computers

Business Context & Client Problem:In a market where product life-cycles are a few months long and competition is heavy, waiting for and relying solely on point-of-sales data was less predictive and constraining in terms of quick course corrections. The PC manufacturer wanted to utilise the market buzz and indications obtained from social media on early days of launch to predict potential growth path of the product.

Impact on Business: The solution provided initial insights on key social media indices to track for assessing performance of a product and react quickly to potential corrective actions. Such solution is expected to be technology enabled and operationalised across various products

Solution: Crawled data from social media sources like Twitter, Amazon, Google etc to create predictive indices around market buzz and consumer sentiments on key features to correlate with potential sales trajectory of launched product.

Creation of Social Indices Build Predictive Model Operationalisation of Solution• Crawling of mentions, reviews, comments from

various sources like Twitter, Amazon & Google reviews, CNET for 9-10 products launched in last 2 years

• Advanced text mining to identify key features and scoring sentiments displayed

• Creation of social indices around mentions, promotion, average reviews, sentiments across key features for each product lifecycle

• Standardisation of growth trajectory of various similar products through parametric curves

• Creation of an advanced panel regression model to relate the social indices and trends with the growth observed over time for various products

• Assessing most predictive factors for relating with growth trajectory and build a scoring model involving various social indices

• Developed set of indices which are highly predictive about product performance

• Operationalising the technology solution

Case Study

Marketing Effectiveness

Measuring Impact of Trade Discount & Promotion

Client: An India based Consumer Packaged Goods Giant

Business Context & Client Problem:The client is a conglomerate of diverse business lines with a significant focus on Consumer Packaged Goods, especially food. In this scenario, the client wanted to measure effectiveness of various trade & consumer promotion on trade revenue with wholesalers, convenience stores and retailers segregating the impact of price change, promotion, competitive actions and cross SKU cannibalisation & Halo effects. .

Impact on Business:The analysis not only revealed hidden patterns of true effectiveness of different promotional spends and cross-category interactions, but also provided enough insights for differentiated promotion strategy across segments

Solution:Segment Data, Create Indices Modeling Decomposition of Impact Analyse Scenarios

• Segmentation of outlets based on similar responsiveness and product assortment

• Creation of Price Indices, Promo calendar, competition indices and cross-category interaction indices

• Treatment of data for trend, seasonality, outlier etc.

• Analyse underlying patterns based on first week or last week of the month, start of year , significant changes etc.

• (Mixed Effect) Regression modeling of volume sold against price, promotion, competition and interaction indices

• Decomposition of volume realised into base volume, promo net impact, cannibalisation & competition impact etc.

• Analyzing RoI of promotion spends based on incremental value

• Identify optimal promotion & price for each channel & segments

Case Study

Marketing Effectiveness

Optimising Marketing Mix

Client: An online education company based in the US, which offers associate degree programs and other certifications based on tie-ups with universities and self generated content

Business Context & Client Problem:

The client organisation deploys a variety of marketing vehicles to generate awareness and demand for their course offerings. These are both online like Display Advertising, Cost-per-action (CPA),Pay-per-click (PPC) arrangements and offline activities like Branding initiatives. There is a need to understand the relative effectiveness and ROI from each of the marketing vehicles, so that the marketing mix can be optimised to get the best return on total marketing spend..

Impact on Business:Based on the model and tool’s suggested marketing mix, there is an estimated lift of 10% in ROI which translates to approximately $1.3 Million on an annualised basis for the current marketing budget

Solution: A market mix model that provides estimated ROI for each of the marketing vehicles and an Optimiser tool that uses the ROI estimates to suggest the ideal marketing mix for a given marketing budget

“Base” Sales & Incremental Effect RoI Estimation for Mktg Vehicles

Optimiser Tool for the Ideal Mktg Mix in a Given

BudgetEstimate “Base” sales and incremental effect due to marketing vehicles

Average and marginal RoI for marketing spend in each vehicle

Directional suggestion of marketing mix changes and incremental RoI

Case Study

Text Mining of Qualitative Inputs in Surveys with our Cloud Based App.

Impact on Business:• A cost effective way to analyse unstructured text data fast and derive actionable insights from it• Increase speed of drawing insights• Simplification of analytics in the hand of business managers

Solution: A comprehensive survey data analysis which support advanced requirements in predictive analytics and text mining with an easy-to-use interface designed for business managers

Input Data Interactive Outputs

Business Context:Responses to open-ended questions in market research and customer surveys typically go unanalysed albeit there are immense insights in them. Our team set out to frame the text analysis problem and create an application that can address all needs of survey analytics in one place. While the application has a broader survey analysis flavour, it has a key textual analysis module that addresses most of the textual analytics needs.Intended Clients: Market Research teams , Customer service , Brand management, Loyalty management, etc

• Any kind of textual data with as many split-by variables

• Capability to handle BIG Data• CSV, Excel formats• Capability to fetch data

automatically from any database

Word Cloud Thematic Analysis

Sentiment Analysis

Interactive Charts

Case Study

Personalised Recommendations

Objective

To identify upto top 5 recommendations for cross-sell program for a retailer

Objective

To identify upto top 5 recommendations for cross-sell program for a retailer

Results

The improvement in precision was 8x using demographic information of customers and 7x using only purchase history

Results

The improvement in precision was 8x using demographic information of customers and 7x using only purchase history

Summary of Retail Data

Precision = Percentage of correct recommendations in all the recommendations

# of Customers 8,419 # of Products 1,559 # of Transactions 58,308 Avg # of Products 5 Time Period (years) 2

Random Recommendations# of Hits Expected # of Hits Precision % Improvement # of Hits Precision % Improvement

1 4,668 14 96 2.06% 686% 115 2.46% 821%2 4,668 28 185 1.98% 661% 204 2.19% 728%3 4,668 42 260 1.86% 619% 266 1.90% 633%5 4,668 70 362 1.55% 517% 374 1.60% 534%

Based on Purchase Data only Based on Purchase and Demographic DataNumber of

RecommendationsCustomers in

Validation Dataset

Random vs Our Recommendation Solution

The approach includes identification of significant demographic attributes influencing customer preferences

Similar techniques could be used to predict customer ratings (e.g. movie, CDs, games, etc.)

The approach could be used to recommend online customers and could used in conjunction with click-stream data

Case Study

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